An Experimental, Tool-Based Evaluation of Requirements Prioritization Techniques in Distributed Settings

In this paper, we compare and analyze three common techniques for prioritizing software requirements: Analytic Hierarchy Process (AHP), Cumulative Voting (CV) and Likert Scale Technique (LST). These techniques are based on a ratio scale and are applied within this research as part of a hierarchical approach to requirements analysis on different levels of abstraction. For the systematic evaluation of these techniques in distributed settings, a controlled experimental setting was developed and carried out via the Internet. Therefore, a particular Web application was developed and data from 199 subjects was collected. The overall results indicate that LST is a simple, fast, and well-scaling prioritization technique, whereas slightly less precise than the other two techniques. However if accuracy is an important criterion, and a more complicated and slower technique is accepted, CV has proven to be most adequate. For the AHP, particularly when used with many requirements, a recommendation cannot be given because of poor scalability

Understanding, Discovering and Leveraging a Software System's Effective Configuration Space

Many modern software systems are highly configurable. While a high degree of configurability has many benefits, such as extensibility, reusability and portability, it also has its costs. In the worst case, the full configuration space of a system is the exponentially large combination of all possible option settings and every configuration can potentially produce unique behavior in the software system. Therefore, this software configuration space explosion problem adds combinatorial complexity to many already difficult software engineering tasks. To date, much of the research in this area has tackled this problem using black-box techniques, such as combinatorial interaction testing (CIT). Although these techniques are promising in systematizing the testing and analysis of configurable systems, they ignore a system's internal structure and we think that is a huge missed opportunity. We hypothesize that systems are often structured such that their effective configuration spaces -- the set of configurations needed to achieve a specific goal -- are often much smaller than their full configuration spaces. And if we can efficiently identify or approximate the effective configuration spaces, then we can use that information to greatly improve various software engineering tasks. To understand the effective configuration spaces of software systems, we used symbolic evaluation, a white-box analysis, to capture all executions a system can take under any configuration. The symbolic evaluation results confirmed that the effective configuration spaces are in fact the composition of many small, self-contained groupings of options. And we developed analysis techniques to succinctly characterize how configurations interact with a system's internal structures. We showed that while the majority of a system's interactions are relatively low strength, some important high-strength interactions do exist, and that existing approaches such as CIT are highly unlikely to generate them in practice. Results from our in-depth investigations serve as the foundation for developing new approaches to efficiently discovering effective configuration spaces. We proposed a new algorithm called interaction tree discovery (iTree) that aims to identify sets of configurations that are smaller than those generated by CIT, while also including important high-strength interactions missed by practical applications of CIT. On each iteration of iTree, we first use low-strength covering array to test the system under, and then apply machine learning techniques to discover new interactions that are potentially responsible for any new coverage seen. By repeating this process, iTree builds up a set of configurations likely to contain key high-strength interactions. We evaluated iTree and our results strongly suggest that iTree can identify high-coverage sets of configurations more effectively than traditional CIT or random sampling. We next developed the interaction learning approach that estimates the configuration interactions underlying the effective configuration space by building classification models for iTree execution results. This approach is light-weight, yet produces accurate estimates of the interactions; making leveraging effective configuration spaces practical for many software engineering tasks. Using this approach, we were able to approximate the effective configuration space of the ~1M-LOC MySQL, something that is infeasible using existing techniques, at very low cost

Validating a model-driven software architecture evaluation and improvement method: A family of experiments

Context: Software architectures should be evaluated during the early stages of software development in order to verify whether the non-functional requirements (NFRs) of the product can be fulfilled. This activity is even more crucial in software product line (SPL) development, since it is also necessary to identify whether the NFRs of a particular product can be achieved by exercising the variation mechanisms provided by the product line architecture or whether additional transformations are required. These issues have motivated us to propose QuaDAI, a method for the derivation, evaluation and improvement of software architectures in model-driven SPL development. Objective: We present in this paper the results of a family of four experiments carried out to empirically validate the evaluation and improvement strategy of QuaDAI. Method: The family of experiments was carried out by 92 participants: Computer Science Master s and undergraduate students from Spain and Italy. The goal was to compare the effectiveness, efficiency, perceived ease of use, perceived usefulness and intention to use with regard to participants using the evaluation and improvement strategy of QuaDAI as opposed to the Architecture Tradeoff Analysis Method (ATAM). Results: The main result was that the participants produced their best results when applying QuaDAI, signifying that the participants obtained architectures with better values for the NFRs faster, and that they found the method easier to use, more useful and more likely to be used. The results of the meta-analysis carried out to aggregate the results obtained in the individual experiments also confirmed these results. Conclusions: The results support the hypothesis that QuaDAI would achieve better results than ATAM in the experiments and that QuaDAI can be considered as a promising approach with which to perform architectural evaluations that occur after the product architecture derivation in model-driven SPL development processes when carried out by novice software evaluators.The authors would like to thank all the participants in the experiments for their selfless involvement in this research. This research is supported by the MULTIPLE Project (MICINN TIN2009-13838) and the ValI+D Program (ACIF/2011/235).González Huerta, J.; Insfrán Pelozo, CE.; Abrahao Gonzales, SM.; Scanniello, G. (2015). Validating a model-driven software architecture evaluation and improvement method: A family of experiments. Information and Software Technology. 57:405-429. https://doi.org/10.1016/j.infsof.2014.05.018S4054295